Implementation of a Federated Large-Scale Remote Sensing Data Sharing Platform

Xuan Ma, Zhibao Wang, Lu Bai, Bingbing Xu, Juntao Gao, Bilong Wen, Jinhua Tao

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

In this paper, a unified virtual cloud storage method for federated data based on the middle layer is proposed, which aims at solving the problems of the heterogeneous data sources of remote sensing data in the shared platform among the federated remote sensing data management application under loose coupling mode. A federated remote sensing image data management model is developed based on NASA's Unified Metadata Model (UMM). This platform implements services such as unified access to multi-source heterogeneous image data which effectively solves the problem of heterogeneous data sources in the loosely coupled federated system, and provides better access methods for upper-level applications.
Original languageEnglish
Title of host publication2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS
PublisherIEEE
Pages5771-5774
Number of pages4
ISBN (Electronic)978-1-6654-0369-6, 978-1-6654-0368-9
ISBN (Print)978-1-6654-4762-1
DOIs
Publication statusPublished - 12 Oct 2021
EventIGARSS 2021 - 2021 IEEE International Geoscience and Remote Sensing Symposium - Brussels, Belgium
Duration: 11 Jul 202116 Jul 2021

Conference

ConferenceIGARSS 2021 - 2021 IEEE International Geoscience and Remote Sensing Symposium
Period11/07/2116/07/21

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